Opensky

OpenSky provides api to get data for flights in progress. It is rate limited

Code

import requests
import pandas as pd
import geopandas

Code

def fetch_opensky_data():
    url = "https://opensky-network.org/api/states/all"
    response = requests.get(url)
    data = response.json()

    columns = [
        "icao24", "callsign", "origin_country", "time_position", "last_contact",
        "longitude", "latitude", "baro_altitude", "on_ground", "velocity",
        "heading", "vertical_rate", "sensors", "geo_altitude", "squawk",
        "spi", "position_source"
    ]

    df_raw = pd.DataFrame(data['states'], columns=columns)
    return (
        df_raw
        .assign(
            time_position=pd.to_datetime(df_raw["time_position"], unit="s"),
            last_contact=pd.to_datetime(df_raw["last_contact"], unit="s"),
        )
    )

df_flights = fetch_opensky_data()


df_flihts_non_nan = df_flights.dropna(subset=["longitude", "latitude"])
gdf = geopandas.GeoDataFrame(
    df_flihts_non_nan,
    geometry=geopandas.points_from_xy(df_flihts_non_nan.longitude, df_flihts_non_nan.latitude),
    crs="EPSG:4326",
)

gdf.explore(fullscreen=True, tooltip=False, popup=True)

Make this Notebook Trusted to load map: File -> Trust Notebook

Flights in world at notebook time

Number of flights by origin_country

Code

(
    df_flights
    .groupby("origin_country")
    .agg(num_flights=("icao24", "count"))
    .sort_values("num_flights", ascending=False)
    .head(20)
)

	num_flights
origin_country
United States	1249
Australia	326
China	261
Germany	254
Ireland	242
Turkey	242
United Kingdom	234
India	221
Japan	188
France	185
Spain	163
Malta	154
Austria	147
Canada	129
Kingdom of the Netherlands	125
United Arab Emirates	116
Switzerland	93
Republic of Korea	86
Poland	86
Thailand	69

Top 20 countries with most flights

All flights

For reference

Code

df_flights

	icao24	callsign	origin_country	time_position	last_contact	longitude	latitude	baro_altitude	on_ground	velocity	heading	vertical_rate	sensors	geo_altitude	squawk	spi	position_source
0	39de4f	TVF29UL	France	2025-05-22 05:31:25	2025-05-22 05:31:26	4.8943	42.5502	11582.40	False	219.99	356.92	0.00	None	11742.42	1000	False	0
1	80162c	AXB3030	India	2025-05-22 05:31:16	2025-05-22 05:31:22	55.6394	22.5508	11887.20	False	237.30	68.11	0.00	None	12702.54	5215	False	0
2	39de4b	TVF32ER	France	2025-05-22 05:31:26	2025-05-22 05:31:26	1.3234	47.2435	8534.40	False	198.85	15.76	0.00	None	8587.74	1000	False	0
3	39de4c	TVF3423	France	2025-05-22 05:31:25	2025-05-22 05:31:25	16.8381	44.6504	11567.16	False	210.49	284.00	-0.33	None	11719.56	5316	False	0
4	407a38	NPT902	United Kingdom	2025-05-22 05:31:25	2025-05-22 05:31:25	-2.4088	49.5136	982.98	False	100.65	215.64	-7.48	None	1089.66	5140	False	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5796	398570	AFR76BR	France	2025-05-22 05:31:25	2025-05-22 05:31:25	3.5937	48.5984	3436.62	False	129.14	302.80	-3.90	None	3489.96	1000	False	0
5797	398571	AFR72EK	France	2025-05-22 05:31:16	2025-05-22 05:31:21	9.1957	48.6884	NaN	True	0.00	75.94	NaN	None	NaN	None	False	0
5798	398572	AFR12GR	France	2025-05-22 05:31:25	2025-05-22 05:31:25	-0.6466	51.3755	8458.20	False	233.96	141.52	9.10	None	8542.02	6312	False	0
5799	a0c7b3	ABX3102	United States	2025-05-22 05:31:25	2025-05-22 05:31:26	-77.7661	41.9711	11285.22	False	254.46	73.80	0.00	None	11330.94	4043	False	0
5800	458666	VKG1512	Denmark	2025-05-22 05:31:26	2025-05-22 05:31:26	10.3125	59.0473	8999.22	False	238.90	186.93	5.85	None	8633.46	6005	False	0

5801 rows × 17 columns